Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements.

BACKGROUND: Accelerometers are useful tools for biologists seeking to gain a deeper understanding of the daily behavior of cryptic species. We describe how we used GPS and tri-axial accelerometer (sampling at 64 Hz) collars to monitor behaviors of free-ranging pumas (Puma concolor), which are difficult or impossible to observe in the wild. We attached collars to twelve pumas in the Santa Cruz Mountains, CA from 2010-2012. By implementing Random Forest models, we classified behaviors in wild pumas based on training data from observations and measurements of captive puma behavior. RESULTS: We applied these models to accelerometer data collected from wild pumas and identified mobile and non-mobile behaviors in captive animals with an accuracy rate greater than 96%. Accuracy remained above 95% even after downsampling our accelerometer data to 16 Hz. We were further able to predict low-acceleration movement behavior (e.g. walking) and high-acceleration movement behavior (e.g. running) with 93.8% and 92% accuracy, respectively. We had difficulty predicting non-movement behaviors such as feeding and grooming due to the small size of our training dataset. Lastly, we used model-predicted and field-verified predation events to quantify acceleration characteristics of puma attacks on large prey. CONCLUSION: These results demonstrate that accelerometers are useful tools for classifying the behaviors of cryptic medium and large-sized terrestrial mammals in their natural habitats and can help scientists gain deeper insight into their fine-scale behavioral patterns. We also show how accelerometer measurements can provide novel insights on the energetics and predation behavior of wild animals. Lastly we discuss the conservation implications of identifying these behavioral patterns in free-ranging species as natural and anthropogenic landscape features influence animal energy allocation and habitat use.

Ecological implications of body composition and thermal capabilities in young antarctic fur seals (Arctocephalus gazella).

In comparison with other homeotherms, young recently weaned marine mammals in high latitudes face exceptional energetic demands when foraging and thermoregulating. Lipids are an important source of energy and a major component of insulation that allows them to meet these demands. To examine the role of lipid stores in a high-latitude pinniped, we measured the body composition and thermoregulatory capabilities of Antarctic fur seal (Arctocephalus gazella) pups and yearlings by using flow-through respirometry and hydrogen isotope dilution. From these data, we constructed a model to examine the importance of postweaning fasting capability in free-ranging young fur seals. Resting metabolic rates were different for pups and yearlings measured in 0.6 degrees C water, 10.3 degrees C water, and ambient air; however, mass and percent lipid as covariates accounted for the different metabolic responses in pups and yearlings for all treatments. The estimated lower critical temperature for combined pups and yearlings was 14.4 degrees C, 10 degrees -15 degrees C above water temperatures normally experienced by Antarctic fur seals. Modeling predicted that a weaned fur seal pup would survive at sea from 9.8 to 36.2 d before succumbing to starvation. The most likely maximum travel distance within this time constraint suggests that food resources close to the natal rookery are important to first-year survival for this species.

A platform for testing and comparing of real-time decision-support algorithms in mobile environments.

The unavailability of a flexible system for realtime testing of decision-support algorithms in a pre-hospital clinical setting has limited their use. In this study, we describe a plug-and-play platform for real-time testing of decision-support algorithms during the transport of trauma casualties en route to a hospital. The platform integrates a standard-of-care vital-signs monitor, which collects numeric and waveform physiologic time-series data, with a rugged ultramobile personal computer. The computer time-stamps and stores data received from the monitor, and performs analysis on the collected data in real-time. Prior to field deployment, we assessed the performance of each component of the platform by using an emulator to simulate a number of possible fault scenarios that could be encountered in the field. Initial testing with the emulator allowed us to identify and fix software inconsistencies and showed that the platform can support a quick development cycle for real-time decision-support algorithms.